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Ocli Api

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EvilFreelancer
ocli-api

Turn any OpenAPI, Swagger, or OpenRPC API into CLI commands and call them. Search endpoints with BM25, check parameters, execute — no MCP server needed.

Overview

PublisherEvilFreelancer
Repositoryopenapi-to-cli
Skill nameocli-api
Stars
265
Forks
16
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by EvilFreelancer on GitHub. Read the source before you install it.

Installation

Install the Ocli Api AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/EvilFreelancer/openapi-to-cli/tree/main/skills/ocli-api
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/EvilFreelancer/openapi-to-cli.git /tmp/openapi-to-cli
mkdir -p .claude/skills
cp -r /tmp/openapi-to-cli/skills/ocli-api .claude/skills/ocli-api
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ocli Api in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ocli Api on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ocli Api is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

ocli — API specification to CLI

Call any HTTP API described by an OpenAPI, Swagger, or OpenRPC spec as CLI commands. No MCP server, no code generation, no JSON schemas in context.

When to use

  • You need to call a REST API (internal, cloud, SaaS)
  • You have an OpenAPI or Swagger spec (URL or local file)
  • You have an OpenRPC JSON or YAML document for a JSON-RPC API
  • You want minimal token overhead (1 tool, ~158 tokens/turn)

Setup (one-time)

bash
npm install -g openapi-to-cli

ocli profiles add <name> \
  --api-base-url <BASE_URL> \
  --openapi-spec <SPEC_URL_OR_PATH> \
  --api-bearer-token "$TOKEN"

ocli use <name>

For OpenRPC, use the same profile command. ocli builds one command per RPC method and wraps documented parameters in a JSON-RPC 2.0 request automatically:

bash
ocli profiles add rpc-api \
  --api-base-url https://api.example.com/rpc \
  --openapi-spec ./openrpc.json

ocli getWidget --widgetId widget-7

ocli serializes integer, number, and boolean values according to the method schema. Pass arrays and objects as JSON. It follows paramStructure: "by-position" by sending the documented flags as a JSON-RPC parameter array in order. Named and either methods receive a JSON object.

For OpenRPC profiles, use rpc:<method> selectors with --include-endpoints or --exclude-endpoints, for example rpc:getWidget. OpenRPC method names must be unique.

Workflow

  1. Search for the right command:
    bash
    ocli commands --query "your task description" --limit 5
  2. Check parameters of the chosen command:
    bash
    ocli <command> --help
  3. Execute the command:
    bash
    ocli <command> --param1 value1 --param2 value2
  4. Parse the JSON response and act on the result.

Search options

bash
# BM25 natural language search
ocli commands --query "upload file to storage" --limit 5

# Regex pattern search
ocli commands --regex "users.*post" --limit 10

# List all commands
ocli commands

Multiple APIs

bash
# Switch active profile
ocli use github

# Or specify per-call
ocli repos_get --profile github --owner octocat --repo Hello-World

Guardrails

  • Always search before guessing a command name.
  • Always check --help before calling a command you haven't used before.
  • Never fabricate parameter names — use the ones from --help output.
  • For OpenRPC, pass only documented RPC parameters. Do not add jsonrpc, method, or id; ocli supplies them and follows the documented parameter structure.
  • If a command returns an error, read the response body before retrying.

Failure handling

  • Command not found: re-search with different keywords or use --regex.
  • Missing required parameter: run --help and add the missing flag.
  • Unknown option: the flag is not defined by the command; copy the exact name from --help, including a leading $ when the spec uses one (--$expand).
  • 401/403: check that the profile has a valid token (ocli profiles show <name>).
  • Spec not loaded: run ocli profiles add again with --openapi-spec to refresh cache.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ocli Api AI skill do?

Turn any OpenAPI, Swagger, or OpenRPC API into CLI commands and call them. Search endpoints with BM25, check parameters, execute — no MCP server needed.

Why use Ocli Api on TypingMind?

Because you install it once and use it with any model. Ocli Api is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ocli Api in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/EvilFreelancer/openapi-to-cli/tree/main/skills/ocli-api. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ocli Api?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ocli Api?

As many as you like. As long as a model supports skills, you can use Ocli Api with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ocli Api AI skill free?

Yes. It is published on GitHub by EvilFreelancer under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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